Abstract
Boxwood blight is a highly invasive emerging disease. Since the first US report in North Carolina and Connecticut in 2011, boxwood blight has spread to over 30 US states, risking more than 90% of boxwood production. A boxwood blight infection risk model was developed from limited studies in controlled environments. Our study investigated the disease field epidemiology and validated the model’s prediction, using leaf wetness estimated by leaf wetness sensor or algorithms, by analysing weekly blight monitoring data collected on detector plants exposed to the prevailing environmental conditions from spring through fall of 2014 to 2017. Boxwood blight was recorded in 61 of 86 weeks, with the highest infected leaf count recorded in late summer or early fall. Rainfall, high relative humidity outside rainy periods and optimal temperatures during prolonged leaf wetness had a significant positive effect on boxwood blight development. Classification analyses showed that disease predictions from the model using leaf wetness estimated by leaf wetness sensor were more closely aligned with observations from the field than predictions based on algorithms. This study improved our understanding of disease field epidemiology, provided leads to improve the existing model, and generated essential knowledge for formulating effective strategies for blight mitigation.